Current works compute framewise F1-Score and IoU scores for key-step localization [1]}, [2]}, [3]}, [4]}, [5]}.
The F1-Score is a harmonic mean of precision and recall scores.
For calculating recall, the ratio between the number of frames having correct key-steps prediction and the number of ground truth key-step frames across all the key-steps of a video is calculated.
For precision, the denominator is the number of frames assigned to the key-steps.
For calculations, the one-to-one mapping between the ground truth and prediction is generated using the Hungarian algorithm [6]} following [7]}, [1]}, [2]}, [4]}, [3]}.
However, these metrics tend to assign high scores to models that assign most frames to a single cluster, as the key-step with most frames matches with the background frame's label in the ground truth.
Furthermore, for untrimmed procedure learning videos, most of the frames are background, resulting in high scores.
